Hands-on Exercise 3

this is the third exercise

First Part

pacman::p_load(ggiraph, plotly, 
               patchwork, DT, tidyverse) 

exam_data <- read_csv("Exam_data.csv")

Rows: 322 Columns: 7
── Column specification ────────────────────────────────────────────────────────
Delimiter: ","
chr (4): ID, CLASS, GENDER, RACE
dbl (3): ENGLISH, MATHS, SCIENCE

ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.

p <- ggplot(data=exam_data, 
       aes(x = MATHS)) +
  geom_dotplot_interactive(
    aes(tooltip = ID),
    stackgroups = TRUE, 
    binwidth = 1, 
    method = "histodot") +
  scale_y_continuous(NULL, 
                     breaks = NULL)
girafe(
  ggobj = p,
  width_svg = 6,
  height_svg = 6*0.618
)

exam_data$tooltip <- c(paste0(     
  "Name = ", exam_data$ID,         
  "\n Class = ", exam_data$CLASS)) 

p <- ggplot(data=exam_data, 
       aes(x = MATHS)) +
  geom_dotplot_interactive(
    aes(tooltip = exam_data$tooltip), 
    stackgroups = TRUE,
    binwidth = 1,
    method = "histodot") +
  scale_y_continuous(NULL,               
                     breaks = NULL)
girafe(
  ggobj = p,
  width_svg = 8,
  height_svg = 8*0.618
)

tooltip_css <- "background-color:white; #<<
font-style:bold; color:black;" #<<

p <- ggplot(data=exam_data, 
       aes(x = MATHS)) +
  geom_dotplot_interactive(              
    aes(tooltip = ID),                   
    stackgroups = TRUE,                  
    binwidth = 1,                        
    method = "histodot") +               
  scale_y_continuous(NULL,               
                     breaks = NULL)
girafe(                                  
  ggobj = p,                             
  width_svg = 6,                         
  height_svg = 6*0.618,
  options = list(    #<<
    opts_tooltip(    #<<
      css = tooltip_css)) #<<
)                                        

tooltip <- function(y, ymax, accuracy = .01) {
  mean <- scales::number(y, accuracy = accuracy)
  sem <- scales::number(ymax - y, accuracy = accuracy)
  paste("Mean maths scores:", mean, "+/-", sem)
}

gg_point <- ggplot(data=exam_data, 
                   aes(x = RACE),
) +
  stat_summary(aes(y = MATHS, 
                   tooltip = after_stat(  
                     tooltip(y, ymax))),  
    fun.data = "mean_se", 
    geom = GeomInteractiveCol,  
    fill = "light blue"
  ) +
  stat_summary(aes(y = MATHS),
    fun.data = mean_se,
    geom = "errorbar", width = 0.2, size = 0.2
  )

Warning: Using `size` aesthetic for lines was deprecated in ggplot2 3.4.0.
ℹ Please use `linewidth` instead.

girafe(ggobj = gg_point,
       width_svg = 8,
       height_svg = 8*0.618)

p <- ggplot(data=exam_data, 
       aes(x = MATHS)) +
  geom_dotplot_interactive(           
    aes(data_id = CLASS),             
    stackgroups = TRUE,               
    binwidth = 1,                        
    method = "histodot") +               
  scale_y_continuous(NULL,               
                     breaks = NULL)
girafe(                                  
  ggobj = p,                             
  width_svg = 6,                         
  height_svg = 6*0.618                      
)                                        

p <- ggplot(data=exam_data, 
       aes(x = MATHS)) +
  geom_dotplot_interactive(              
    aes(data_id = CLASS),              
    stackgroups = TRUE,                  
    binwidth = 1,                        
    method = "histodot") +               
  scale_y_continuous(NULL,               
                     breaks = NULL)
girafe(                                  
  ggobj = p,                             
  width_svg = 6,                         
  height_svg = 6*0.618,
  options = list(                        
    opts_hover(css = "fill: #202020;"),  
    opts_hover_inv(css = "opacity:0.2;") 
  )                                        
)                                        

p <- ggplot(data=exam_data, 
       aes(x = MATHS)) +
  geom_dotplot_interactive(              
    aes(tooltip = CLASS, 
        data_id = CLASS),              
    stackgroups = TRUE,                  
    binwidth = 1,                        
    method = "histodot") +               
  scale_y_continuous(NULL,               
                     breaks = NULL)
girafe(                                  
  ggobj = p,                             
  width_svg = 6,                         
  height_svg = 6*0.618,
  options = list(                        
    opts_hover(css = "fill: #202020;"),  
    opts_hover_inv(css = "opacity:0.2;") 
  )                                        
)                                        

exam_data$onclick <- sprintf("window.open(\"%s%s\")",
"https://www.moe.gov.sg/schoolfinder?journey=Primary%20school",
as.character(exam_data$ID))

p <- ggplot(data=exam_data, 
       aes(x = MATHS)) +
  geom_dotplot_interactive(              
    aes(onclick = onclick),              
    stackgroups = TRUE,                  
    binwidth = 1,                        
    method = "histodot") +               
  scale_y_continuous(NULL,               
                     breaks = NULL)
girafe(                                  
  ggobj = p,                             
  width_svg = 6,                         
  height_svg = 6*0.618)                                        

p1 <- ggplot(data=exam_data, 
       aes(x = MATHS)) +
  geom_dotplot_interactive(              
    aes(data_id = ID),              
    stackgroups = TRUE,                  
    binwidth = 1,                        
    method = "histodot") +  
  coord_cartesian(xlim=c(0,100)) + 
  scale_y_continuous(NULL,               
                     breaks = NULL)

p2 <- ggplot(data=exam_data, 
       aes(x = ENGLISH)) +
  geom_dotplot_interactive(              
    aes(data_id = ID),              
    stackgroups = TRUE,                  
    binwidth = 1,                        
    method = "histodot") + 
  coord_cartesian(xlim=c(0,100)) + 
  scale_y_continuous(NULL,               
                     breaks = NULL)

girafe(code = print(p1 + p2), 
       width_svg = 6,
       height_svg = 3,
       options = list(
         opts_hover(css = "fill: #202020;"),
         opts_hover_inv(css = "opacity:0.2;")
         )
       ) 

plot_ly(data = exam_data, 
             x = ~MATHS, 
             y = ~ENGLISH)

No trace type specified:
  Based on info supplied, a 'scatter' trace seems appropriate.
  Read more about this trace type -> https://plotly.com/r/reference/#scatter

No scatter mode specifed:
  Setting the mode to markers
  Read more about this attribute -> https://plotly.com/r/reference/#scatter-mode

plot_ly(data = exam_data, 
        x = ~ENGLISH, 
        y = ~MATHS, 
        color = ~RACE)

No trace type specified:
  Based on info supplied, a 'scatter' trace seems appropriate.
  Read more about this trace type -> https://plotly.com/r/reference/#scatter
No scatter mode specifed:
  Setting the mode to markers
  Read more about this attribute -> https://plotly.com/r/reference/#scatter-mode

p <- ggplot(data=exam_data, 
            aes(x = MATHS,
                y = ENGLISH)) +
  geom_point(size=1) +
  coord_cartesian(xlim=c(0,100),
                  ylim=c(0,100))
ggplotly(p)

d <- highlight_key(exam_data)
p1 <- ggplot(data=d, 
            aes(x = MATHS,
                y = ENGLISH)) +
  geom_point(size=1) +
  coord_cartesian(xlim=c(0,100),
                  ylim=c(0,100))

p2 <- ggplot(data=d, 
            aes(x = MATHS,
                y = SCIENCE)) +
  geom_point(size=1) +
  coord_cartesian(xlim=c(0,100),
                  ylim=c(0,100))
subplot(ggplotly(p1),
        ggplotly(p2))

DT::datatable(exam_data, class= "compact")

d <- highlight_key(exam_data) 
p <- ggplot(d, 
            aes(ENGLISH, 
                MATHS)) + 
  geom_point(size=1) +
  coord_cartesian(xlim=c(0,100),
                  ylim=c(0,100))

gg <- highlight(ggplotly(p),        
                "plotly_selected")  

crosstalk::bscols(gg,               
                  DT::datatable(d), 
                  widths = 5)        

Setting the `off` event (i.e., 'plotly_deselect') to match the `on` event (i.e., 'plotly_selected'). You can change this default via the `highlight()` function.

d <- highlight_key(exam_data) 
p <- ggplot(d, 
            aes(ENGLISH, 
                MATHS)) + 
  geom_point(size=1) +
  coord_cartesian(xlim=c(0,100),
                  ylim=c(0,100))

gg <- highlight(ggplotly(p),        
                "plotly_selected")  

crosstalk::bscols(gg,               
                  DT::datatable(d), 
                  widths = 5)        

Setting the `off` event (i.e., 'plotly_deselect') to match the `on` event (i.e., 'plotly_selected'). You can change this default via the `highlight()` function.

Second Part

pacman::p_load(readxl, gifski, gapminder,
               plotly, gganimate, tidyverse)

pacman::p_load(tidyverse, gganimate, plotly, gifski)

# 1. Import Data
raw_data <- read_csv("respopagesextod2025.csv")

Rows: 100928 Columns: 7
── Column specification ────────────────────────────────────────────────────────
Delimiter: ","
chr (5): PA, SZ, AG, Sex, TOD
dbl (2): Pop, Time

ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.

# 2. Data Transformation (Wrangling)
# We need to pivot the data from "Long" (Age Groups in rows) to "Wide" (Young/Old columns)
pop_data <- raw_data %>%
  # Create a simplified Age Category flag
  mutate(Age_Category = case_when(
    AG %in% c("0_to_4", "5_to_9", "10_to_14") ~ "Young",
    AG %in% c("65_to_69", "70_to_74", "75_to_79", "80_to_84", "85_to_89", "90_and_over") ~ "Old",
    TRUE ~ "Working_Age"
  )) %>%
  # Group by Planning Area (PA) and Time to aggregate
  group_by(PA, Time) %>%
  summarise(
    Total_Pop = sum(Pop),
    Young_Pop = sum(Pop[Age_Category == "Young"]),
    Old_Pop = sum(Pop[Age_Category == "Old"]),
    .groups = 'drop'
  ) %>%
  # Filter out areas with 0 population to avoid errors
  filter(Total_Pop > 0) %>%
  # Calculate Percentages
  mutate(Percent_Young = (Young_Pop / Total_Pop) * 100,
         Percent_Old = (Old_Pop / Total_Pop) * 100)

# Inspect the result
head(pop_data)

# A tibble: 6 × 7
  PA             Time Total_Pop Young_Pop Old_Pop Percent_Young Percent_Old
  <chr>         <dbl>     <dbl>     <dbl>   <dbl>         <dbl>       <dbl>
1 Ang Mo Kio     2025    159380     16380   41960          10.3        26.3
2 Bedok          2025    274650     31820   65060          11.6        23.7
3 Bishan         2025     87610     10850   19930          12.4        22.7
4 Bukit Batok    2025    166150     22890   30520          13.8        18.4
5 Bukit Merah    2025    146790     16190   36920          11.0        25.2
6 Bukit Panjang  2025    136720     18070   24690          13.2        18.1

# Create the base static plot
p <- ggplot(pop_data, aes(x = Percent_Old, y = Percent_Young, 
                          size = Total_Pop, 
                          colour = PA)) +
  geom_point(alpha = 0.7, 
             show.legend = FALSE) +
  scale_size(range = c(2, 12)) +
  labs(title = 'Year: {frame_time}', 
       x = '% Aged (65+)', 
       y = '% Young (0-14)') +
  theme_minimal()

# Create interactive plot using ggplotly
# Note: We include 'frame = Time' and 'ids = PA' for the animation logic
gg <- ggplot(pop_data, aes(x = Percent_Old, y = Percent_Young, 
                           size = Total_Pop, 
                           colour = PA)) +
  geom_point(aes(size = Total_Pop,
                ),
             alpha = 0.7) +
  scale_size(range = c(2, 12)) +
  labs(x = '% Aged', 
       y = '% Young') + 
  theme(legend.position='none')

ggplotly(gg)

ggplot(pop_data, aes(x = Percent_Old, y = Percent_Young, 
                     size = Total_Pop, 
                     colour = PA)) +
  geom_point(alpha = 0.7, 
             show.legend = FALSE) +
  # scale_colour_manual(values = country_colors) +  <-- Removed: Incompatible with PA data
  scale_size(range = c(2, 12)) +
  labs(title = 'Year: {frame_time}', 
       x = '% Aged', 
       y = '% Young')

# Load necessary libraries
pacman::p_load(tidyverse, gganimate)

# 1. Import
raw_data <- read_csv("respopagesextod2025.csv")

Rows: 100928 Columns: 7
── Column specification ────────────────────────────────────────────────────────
Delimiter: ","
chr (5): PA, SZ, AG, Sex, TOD
dbl (2): Pop, Time

ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.

# 2. Transform to calculate % Young and % Old per Planning Area
pop_data <- raw_data %>%
  mutate(Age_Category = case_when(
    AG %in% c("0_to_4", "5_to_9", "10_to_14") ~ "Young",
    AG %in% c("65_to_69", "70_to_74", "75_to_79", "80_to_84", "85_to_89", "90_and_over") ~ "Old",
    TRUE ~ "Working_Age"
  )) %>%
  group_by(PA, Time) %>%
  summarise(
    Total_Pop = sum(Pop),
    Young_Pop = sum(Pop[Age_Category == "Young"]),
    Old_Pop = sum(Pop[Age_Category == "Old"]),
    .groups = 'drop'
  ) %>%
  filter(Total_Pop > 0) %>%
  mutate(Percent_Young = (Young_Pop / Total_Pop) * 100,
         Percent_Old = (Old_Pop / Total_Pop) * 100)

gg <- ggplot(pop_data, 
       aes(x = Percent_Old, 
           y = Percent_Young, 
           size = Total_Pop, 
           colour = PA)) +
  geom_point(aes(size = Total_Pop,
                 ),
             alpha = 0.7, 
             show.legend = FALSE) +
  scale_size(range = c(2, 12)) +
  labs(x = '% Aged', 
       y = '% Young')

ggplotly(gg)

gg <- ggplot(pop_data, 
       aes(x = Percent_Old, 
           y = Percent_Young, 
           size = Total_Pop, 
           colour = PA)) +
  geom_point(aes(size = Total_Pop,
                 ),
             alpha = 0.7) +
  scale_size(range = c(2, 12)) +
  labs(x = '% Aged', 
       y = '% Young') + 
  theme(legend.position='none')

ggplotly(gg)

bp <- pop_data %>%
  plot_ly(x = ~Percent_Old, 
          y = ~Percent_Young, 
          size = ~Total_Pop, 
          color = ~PA,
          sizes = c(2, 100),
          frame = ~Time, 
          text = ~PA, 
          hoverinfo = "text",
          type = 'scatter',
          mode = 'markers'
          ) %>%
  layout(showlegend = FALSE)
bp

Warning in RColorBrewer::brewer.pal(max(N, 3L), "Set2"): n too large, allowed maximum for palette Set2 is 8
Returning the palette you asked for with that many colors
Warning in RColorBrewer::brewer.pal(max(N, 3L), "Set2"): n too large, allowed maximum for palette Set2 is 8
Returning the palette you asked for with that many colors

ggplot(pop_data, aes(x = Percent_Old, y = Percent_Young, 
                     size = Total_Pop, 
                     colour = PA)) +
  geom_point(alpha = 0.7, 
             show.legend = FALSE) +
  scale_size(range = c(2, 12)) +
  labs(title = 'Year: 2025',  # Hardcoded since it's only 2025
       x = '% Aged', 
       y = '% Young')

pacman::p_load(readxl, gifski, gapminder,
               plotly, gganimate, tidyverse)

col <- c("Country", "Continent")
globalPop <- read_xls("GlobalPopulation.xls",
                      sheet="Data") %>%
  mutate_at(col, as.factor) %>%
  mutate(Year = as.integer(Year))

ggplot(globalPop, aes(x = Old, y = Young, 
                      size = Population, 
                      colour = Country)) +
  geom_point(alpha = 0.7, 
             show.legend = FALSE) +
  scale_colour_manual(values = country_colors) +
  scale_size(range = c(2, 12)) +
  labs(title = 'Year: {frame_time}', 
       x = '% Aged', 
       y = '% Young') 

gg <- ggplot(globalPop, 
       aes(x = Old, 
           y = Young, 
           size = Population, 
           colour = Country)) +
  geom_point(aes(size = Population,
                 ),
             alpha = 0.7, 
             show.legend = FALSE) +
  scale_colour_manual(values = country_colors) +
  scale_size(range = c(2, 12)) +
  labs(x = '% Aged', 
       y = '% Young')

ggplotly(gg)

# 1. Load Packages

pacman::p_load(tidyverse, plotly, gganimate)



# 2. Import and Transform Data

# We first calculate the % Young and % Old for each area

raw_data <- read_csv("respopagesextod2025.csv")

Rows: 100928 Columns: 7
── Column specification ────────────────────────────────────────────────────────
Delimiter: ","
chr (5): PA, SZ, AG, Sex, TOD
dbl (2): Pop, Time

ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.

pop_data <- raw_data %>%

  mutate(Age_Category = case_when(

    AG %in% c("0_to_4", "5_to_9", "10_to_14") ~ "Young",

    AG %in% c("65_to_69", "70_to_74", "75_to_79", "80_to_84", "85_to_89", "90_and_over") ~ "Old",

    TRUE ~ "Working_Age"

  )) %>%

  group_by(PA, Time) %>%

  summarise(

    Total_Pop = sum(Pop),

    Young_Pop = sum(Pop[Age_Category == "Young"]),

    Old_Pop = sum(Pop[Age_Category == "Old"]),

    .groups = 'drop'

  ) %>%

  filter(Total_Pop > 0) %>%

  mutate(Percent_Young = (Young_Pop / Total_Pop) * 100,

         Percent_Old = (Old_Pop / Total_Pop) * 100)



# 3. (Optional) Simulate Data for 2020

# We create a fake 2020 dataset so the Play button actually moves dots!

pop_data_simulated <- pop_data %>%

  mutate(Time = 2025) %>%

  bind_rows(

    pop_data %>% 

      mutate(Time = 2020, 

             Percent_Old = Percent_Old * 0.85,    # Pretend 2020 was younger

             Percent_Young = Percent_Young * 1.1) # Pretend 2020 had more kids

  )



# 4. Build the Animated Plot using ggplotly method

# Note: usage of frame = Time creates the slider

# Note: usage of theme(legend.position='none') removes the legend

gg <- ggplot(pop_data_simulated, 

             aes(x = Percent_Old, 

                 y = Percent_Young, 

                 size = Total_Pop, 

                 colour = PA)) +

  geom_point(aes(size = Total_Pop,

               ),      # Ensures smooth transition of specific bubbles

             alpha = 0.7) +

  scale_size(range = c(2, 12)) +

  labs(x = '% Aged', 

       y = '% Young') +

  theme(legend.position='none') # Removes the legend as requested



# 5. Generate the Interactive Plot

ggplotly(gg)

gg <- ggplot(globalPop, 
       aes(x = Old, 
           y = Young, 
           size = Population, 
           colour = Country)) +
  geom_point(aes(size = Population,
                 ),
             alpha = 0.7) +
  scale_colour_manual(values = country_colors) +
  scale_size(range = c(2, 12)) +
  labs(x = '% Aged', 
       y = '% Young') + 
  theme(legend.position='none')

ggplotly(gg)

bp <- globalPop %>%
  plot_ly(x = ~Old, 
          y = ~Young, 
          size = ~Population, 
          color = ~Continent,
          sizes = c(2, 100),
          frame = ~Year, 
          text = ~Country, 
          hoverinfo = "text",
          type = 'scatter',
          mode = 'markers'
          ) %>%
  layout(showlegend = FALSE)
bp

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# 1. Load Packages
pacman::p_load(tidyverse, plotly)

# 2. Build the Plot (Clean Version)
bp <- globalPop %>%
  plot_ly(x = ~Old, 
          y = ~Young, 
          size = ~Population, 
          color = ~Continent,
          sizes = c(2, 100),
          frame = ~Year, 
          text = ~Country, 
          hoverinfo = "text",
          type = 'scatter',
          mode = 'markers',
          # FIX: Explicitly define marker line width to 0 to stop the warning
          marker = list(sizemode = 'diameter', 
                        opacity = 0.7,
                        line = list(width = 0)) 
          ) %>%
  layout(showlegend = FALSE,
         xaxis = list(title = '% Aged'),
         yaxis = list(title = '% Young'),
         title = "Global Population Dynamics")

# 3. Display the plot
bp

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